An enterprise typically utilizes a controller to manage the financial affairs of the enterprise. Duties of the controller may comprise monitoring data, such as employee timecards, account receivables (A/R), inventory levels, sales order, production reports, and the like. A controller may use this data to generate forecasts or trends, create reports for budget or production analysis, and/or general strategy analysis.
Currently, the controlling process at an enterprise is done either manually or is semi-automated. For example, in a monthly controlling process, the controller may collect actual data for the purpose of comparison to plan data. Actual data refers to data that has been recorded and based upon an already executed transactions. For example, actual data may include monthly sales data or production data that have been recorded for an already completed month. Plan data refers to preset goals, forecasts or targets, such as the amount of sales or level of production that should be achieved for a particular period of time. Actual data may be gathered from diverse sources, such as spreadsheets created by individual departments (e.g., sales and production), e-mails, collaborative meetings, handwritten notes, and so forth. Specific examples of data collected include open sales orders, revenue information, order cancellations, inventory levels, pipeline information, trend information, and last minute information/updates. The actual data may then be consolidated in order that it may be analyzed by the controller for any deviations from the plan data.
To correct a deviation between the actual data and the plan data, the root cause of the deviation must be understood. However, this process may be very difficult and time consuming. For instance, because the data was consolidated, there is often no link to the transactional or even reporting details contributing to the result. Therefore, additional meetings and communications between representatives from the various departments in an enterprise are needed to determine the root cause of the deviation. When the root cause of the deviation is discovered it may be added to the report for later review at, for example, a sales and operations meeting.
The sales and operations meeting, which may occur once a month, allows the responsible department representatives and the controller to discuss deviations from the plan data and possible corrective actions. In most cases, there is no immediate decisions made during the sales and operations meeting. Instead, options are discussed and tasks are distributed for clarification and subsequent reporting to the controller who makes the final call on corrective actions. This final call is based on all the feedback information that the controller collects. After the corrective action is chosen, the controller may further designate follow-up tasks.
As a result of the large amount of data and constant communications that are necessary for a controller to perform his or her job, a controller may not have enough time to perform their job effectively. In particular, the most time consuming exercises performed by a controller are data consolidation and data gathering, analysis of data, system maintenance, collaboration, and providing management summaries.
Further, current controlling processes are incapable of understanding the analytical models of operational drivers (e.g., occurring sales transactions) and their impact on overall revenue. For example, the current controlling processes lack the ability to utilize projected data to project a loss of revenue compared to plan revenue for a predetermined time period. Projected data refers to data that is arrived at by extrapolating actual data and historical performances to generate a trend of where the actual data will be at a specified instance in future time. With these current processes, the controller can only be alerted of this loss at the end of the period when all the actual data necessary to show the loss is available.
Accordingly, there is a need for a solution that utilizes an enterprise application capable of automating and centralizing the financial operation steering process through interaction with, for example, an enterprise resource planning (ERP) system. Such a solution should enable a controller to take a proactive approach to emerging variances. In particular, a solution is needed to support a controller to gather data, identify deviations, resolve deviations, and perform follow-ups. Further, it would be beneficial to provide an alert mechanism to alert the controller of a resulting deviation. The alert mechanism may be based on analytics that are conceptualized in a projection plan and presented relative to the actual plan.